Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 24; p. 6400
• Main Authors: Liu, Shangqin, Zhao, Xizhi, Zhang, Fuhao, Qiu, Agen, Chen, Liujia, Huang, Jing, Chen, Song, Zhang, Shu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Algorithms; Economic growth; Environmental protection; GDP; Gross Domestic Product; Imaging radiometers; Infrared imaging; Infrared radiometers; Light; Meteorological satellites; Multiscale Geographic Weighted Regression; Night; Nighttime; NPP-VIIRS; NTL data; Population studies; Radiometry; Regional development; Regression; Remote sensing; Root-mean-square errors; Spatial discrimination; spatial downscaling; Spatial resolution; Temporal resolution; Vegetation; Beijing China; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14246400

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R2 (R2 = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications.